Objectives: This article describes novel magnetic resonance imaging (MRI)-compatible focused ultrasound robotic systems and agar-based MRI-compatible ultrasonic phantoms mimicking bone. Materials and Methods: All the robotic systems and phantoms were developed using three-dimensional (3D) printing technology using plastic material. The tissue surrounding the bone in the phantoms was mimicked using agar-based solutions. Results: The article presents MRI-guided focused ultrasound robotic systems for brain, prostate, and gynecological targets. It also reports on MRI-compatible ultrasonic phantoms for brain, breast, bone, and motion. Conclusions: The popular 3D printing technology serves a major role in MRI-guided focused ultrasound surgery because MRI-guided focused ultrasound robotic systems can be developed. In addition, 3D printing can be used to develop MR-compatible phantoms that include bone structures for testing the safety and efficacy of focused ultrasound applications. All the developed structures have been evaluated in MRI environment using either mimicking materials or animals.

Background and Objectives: In this article, we propose an image segmentation model based on Chan-Vese (CV) for image segmentation. By taking into account the local features of the image, the new proposed model can successfully segment images with intensity nonuniformity. Materials and Methods: We quantitatively compare our method with other two state-of-the-art algorithms, namely, CV model and local binary fitting (LBF) model in segmenting synthetic MR images with the ground truth from BrainWeb; the data can be available at: https://www.mni/mcgill.ca/brainweb/. For segmenting the missing and weak boundaries, to deal with the intensity inhomogeneity, based on the LBF model, we introduced the convex total variation regularization term, for explicit smoothing of the level set function ø. The evolution equation will be solved through the level set method of calculus of variations. Results: In the experimental processing, we use some real images and magnetic resonance imaging brain images as the experimental images, to validate the stabilization of algorithm. The experimental results on comprehensive and sincerity images show the outstanding of our proposed model with reference to stabilization and availability. Conclusions: We propose a new segmentation local information of an image and introduce a new regularization functional is to keep the level set function smooth. Finally, various experimental results on real and low-contrast image, showing which is a powerful type of images, including some that would be difficult to segment with gradient-based methods. In addition, the advantages of the proposed model are better than CV model and the LBF model. Our new model can effectively segment a real image.

Background and Objectives: Cervical total disc replacement (TDR) is a novel dynamically stabilizing technique for the symptomatic cervical intervertebral segment. While the long-term effect of mainstream cervical nonconstrained artificial disc group (CNAD) does not match the theoretical effects of mobility preserving and neural decompression. The cervical semiconstrained elastic integrated artificial disc (CSID) may be a more reasonable design. However, beneficial or adverse effects of this design have not been measured and data for biomechanical effect are unavailable. The aim of this study is to assess the biomechanical effect of CSID on the segmental motion at implanted and adjacent levels. Methods: This study was supported by medical science developmental funding of Nanjing (20,000 dollars). Eight cadaveric C3–T1 specimens were loaded in flexion/extension (F/E), axial rotation (AR), and lateral bending (LB) with CSID, CNAD, and anterior fusion (AF) implanted at C5–C6 level alternatively. The range of motion (ROM), neutral zone (NZ), and elastic zone (EZ) at implanted and adjacent levels were measured. The mean values of parameters in the intact specimen group (INT), CSID group, CNAD group, and AF group were compared statistically (n = 8). Results: There was no significant difference of ROM, NZ, and EZ at implanted and adjacent levels between CSID and INT in F/E, AR, and LB (P > 0.05). CNAD caused a significant change of EZ in F/E and LB and ROM in LB at implanted level. Meantime, CNAD caused ROM increasing at adjacent levels (P < 0.05). AF caused the most significant changes of ROM, NZ, and EZ in F/E, AR and LB, compared to CSID and CNAD (P < 0.05). Conclusions: The semiconstrained elastic integrated design of cervical artificial disc may mimic of physiological disc's biomechanical effects on segmental kinematics at implanted and adjacent levels more closely, compared to nonconstrained discs and AF. CSID disc may reduce the acceleration of postTDR degeneration at the implanted and adjacent levels due to this promoted biomechanical performance. CSID disc could be a potential candidate for future cervical artificial intervertebral prosthesis studies.

Background: In this research, the pulse wave data of 274 subjects from both the patient and control groups are evaluated and analyzed. Based on the pulse wave analysis of those subjects, a study of diagnosing cardiovascular diseases is conducted. Methods and Results: By investigating the correlation between the cardiac indices Reverse Shoulder Index (RSI) and Ratio of Distance for patients with cardiovascular diseases from different age and gender groups, several common and important observations are reported. By carrying out case studies, we have verified some of our findings with several patient cases. Conclusion: In this research, pulse wave analysis is applied for the study of cardiovascular diseases with some important observations. We expect that our discoveries in this research can eventually help the end-users in cardiovascular diseases diagnosis.

Background and Objectives: The diagnosis of cancer is concerned, and the prediction of cell carcinoma is of great importance for the treatment. Materials and Methods: First, we obtain a series of slices of tumor cell pathology in clinical data, with being followed training sets and test sets gained by adding data model. Then, we design a convolutional neural network training and prediction model. After that, we optimize parameters for training and prediction model, combining experience. Results: In experiment, the accuracy of the model predicting for cell carcinoma is 87.38%. Conclusions: This study provides a reference that predicts the extent of cell carcinoma progression by using deep learning model.